Automatic analysis of nuclear features reveals a non-tumoral predictor of tumor grade in bladder cancer.

BACKGROUND & OBJECTIVES: Tumor grade determines prognosis in urothelial carcinoma. The classification of low and high grade is based on nuclear morphological features that include nuclear size, hyperchromasia and pleomorphism. These features are subjectively assessed by the pathologists and are not numerically measured, which leads to high rates of interobserver variability. The purpose of this study is to assess the value of a computer-based image analysis tool for identifying predictors of tumor grade in bladder cancer. METHODS: Four hundred images of urothelial tumors were graded by five pathologists and two expert genitourinary pathologists using a scale of 1 (lowest grade) to 5 (highest grade). A computer algorithm was used to automatically segment the nuclei and to provide morphometric parameters for each nucleus, which were used to establish the grading algorithm. Grading algorithm was compared to pathologists' agreement. RESULTS: Comparison of the grading scores of the five pathologists with the expert genitourinary pathologists score showed agreement rates between 88.5% and 97.5%.The agreement rate between the two expert genitourinary pathologists was 99.5%. The quantified algorithm based conventional parameters that determine the grade (nuclear size, pleomorphism and hyperchromasia) showed > 85% agreement with the expert genitourinary pathologists. Surprisingly, the parameter that was most associated with tumor grade was the 10th percentile of the nuclear area, and high grade was associated with lower 10th percentile nuclei, caused by the presence of more inflammatory cells in the high-grade tumors. CONCLUSION: Quantitative nuclear features could be applied to determine urothelial carcinoma grade and explore new biologically explainable parameters with better correlation to grade than those currently used.

Recommendations for reporting tissue and circulating tumour (ct)DNA next-generation sequencing results in non-small cell lung cancer.

Non-small cell lung cancer is a heterogeneous disease and molecular characterisation plays an important role in its clinical management. Next-generation sequencing-based panel testing enables many molecular alterations to be interrogated simultaneously, allowing for comprehensive identification of actionable oncogenic drivers (and co-mutations) and appropriate matching of patients with targeted therapies. Despite consensus in international guidelines on the importance of broad molecular profiling, adoption of next-generation sequencing varies globally. One of the barriers to its successful implementation is a lack of accepted standards and guidelines specifically for the reporting and clinical annotation of next-generation sequencing results. Based on roundtable discussions between pathologists and oncologists, we provide best practice recommendations for the reporting of next-generation sequencing results in non-small cell lung cancer to facilitate its use and enable easy interpretation for physicians. These are intended to complement existing guidelines related to the use of next-generation sequencing (solid and liquid). Here, we discuss next-generation sequencing workflows, the structure of next-generation sequencing reports, and our recommendations for best practice thereof. The aim of these recommendations and considerations is ultimately to ensure that reports are fully interpretable, and that the most appropriate treatment options are selected based on robust molecular profiles in well-defined reports.

Detection of Muscularis propria Invasion in Urothelial Carcinoma Using Artificial Intelligence.

Background & Objective: Assessment of muscularis propria invasion is a crucial step in the management of urothelial carcinoma since it necessitates aggressive treatment. The diagnosis of muscle invasion is a challenging process for pathologists. Artificial intelligence is developing rapidly and being implemented in various fields of pathology. The purpose of this study was to develop an algorithm for the detection of muscularis propria invasion in urothelial carcinoma. Methods: The Training cohort consisted of 925 images from 50 specimens of urothelial carcinoma. Ninety-seven images from 10 new specimens were used as a validation cohort. Clinical validation used 127 whole specimens with a total of 617 slides. The algorithm determined areas where tumor and muscularis propria events were in nearest proximity, and presented these areas to the pathologist. Results: Analytical evaluation showed a sensitivity of 72% for muscularis propria and 65% for tumor, and a specificity of 46% and 77% for muscularis propria and tumor detection, respectively. The incorporation of the spatial proximity factor between muscularis propria and tumor in the clinical validation significantly improved the detection of muscularis propria invasion, as the algorithm managed to identify all except for one case with muscle invasive bladder cancer in the clinical validation cohort. The case missed by the algorithm was nested urothelial carcinoma, a rare subtype with unusual morphologic features. The pathologist managed to identify muscle invasion based on the images provided by the algorithm in a short time, with an average of approximately 5 s. Conclusion: The algorithm we developed may greatly aid in accurate identification of muscularis propria invasion by imitating the thought process of the pathologist.

Algorithm-assisted diagnosis of Hirschsprung's disease - evaluation of robustness and comparative image analysis on data from various labs and slide scanners.

BACKGROUND: Differences in the preparation, staining and scanning of digital pathology slides create significant pre-analytic variability. Algorithm-assisted tools must be able to contend with this variability in order to be applicable in clinical practice. In a previous study, a decision support algorithm was developed to assist in the diagnosis of Hirschsprung's disease. In the current study, we tested the robustness of this algorithm while assessing for pre-analytic factors which may affect its performance. METHODS: The decision support algorithm was used on digital pathology slides obtained from four different medical centers (A-D) and scanned by three different scanner models (by Philips, Hamamatsu and 3DHISTECH). A total of 192 cases and 1782 slides were used in this study. RGB histograms were constructed to compare images from the various medical centers and scanner models and highlight the differences in color and contrast. RESULTS: The algorithm was able to correctly identify ganglion cells in 99.2% of cases, from all medical centers (All scanned by the Philips slide scanner) as well as 95.5% and 100% of the slides scanned by the 3DHISTECH and Hamamatsu brand slide scanners, respectively. The total error rate for center D was lower than the other medical centers (3.9% vs 7.1%, 10.8% and 6% for centers A-C, respectively), the vast majority of errors being false positives (3.45% vs 0.45% false negatives). The other medical centers showed a higher rate of false negatives in relation to false positives (6.81% vs 0.29%, 9.8% vs 1.2% and 5.37% vs 0.63% for centers A-C, respectively). The total error rates for the Philips, Hamamatsu and 3DHISTECH brand scanners were 3.9%, 3.2% and 9.8%, respectively. RGB histograms demonstrated significant differences in pixel value distribution between the four medical centers, as well as between the 3DHISTECH brand scanner when compared to the Philips and Hamamatsu brand scanners. CONCLUSIONS: The results reported in this paper suggest that the algorithm-based decision support system has sufficient robustness to be applicable for clinical practice. In addition, the novel method used in its development - Hierarchial-Contexual Analysis (HCA) may be applicable to the development of algorithm-assisted tools in other diseases, for which available datasets are limited. Validation of any given algorithm-assisted support system should nonetheless include data from as many medical centers and scanner models as possible.

Image-Based Deep Learning Detection of High-Grade B-Cell Lymphomas Directly from Hematoxylin and Eosin Images.

Deep learning applications are emerging as promising new tools that can support the diagnosis and classification of different cancer types. While such solutions hold great potential for hematological malignancies, there have been limited studies describing the use of such applications in this field. The rapid diagnosis of double/triple-hit lymphomas (DHLs/THLs) involving MYC, BCL2 and/or BCL6 rearrangements is obligatory for optimal patient care. Here, we present a novel deep learning tool for diagnosing DHLs/THLs directly from scanned images of biopsy slides. A total of 57 biopsies, including 32 in a training set (including five DH lymphoma cases) and 25 in a validation set (including 10 DH/TH cases), were included. The DHL-classifier demonstrated a sensitivity of 100%, a specificity of 87% and an AUC of 0.95, with only two false positive cases, compared to FISH. The DHL-classifier showed a 92% predictive value as a screening tool for performing conventional FISH analysis, over-performing currently used criteria. The work presented here provides the proof of concept for the potential use of an AI tool for the identification of DH/TH events. However, more extensive follow-up studies are required to assess the robustness of this tool and achieve high performances in a diverse population.

Clear Cell Renal Cell Carcinoma with Prominent Micropapillary Pattern: A Case Report of a Previously Undescribed Morphology.

The classic morphology of clear cell renal cell carcinoma consists of nests of cells with clear cytoplasm. Nevertheless, other histologic patterns may be seen including cells with eosinophilic cytoplasm, bizarre multinucleated giant tumor cells and pseudopapillary structures. In this article, we present the first case of clear cell renal cell carcinoma with a prominent micropapillary pattern.

Mutated TP53 in Circulating Tumor DNA as a Risk Level Biomarker in Head and Neck Squamous Cell Carcinoma Patients.

Circulating tumor DNA (ctDNA) has been suggested as a surrogate biomarker for early detection of cancer recurrence. We aimed to explore the utility of ctDNA as a noninvasive prognostic biomarker in newly diagnosed head and neck squamous cell carcinoma (HNSCC) patients. Seventy HNSCC specimens were analysed for the detection of TP53 genetic alterations utilizing next-generation sequencing (NGS). TP53 mutations were revealed in 55 (79%). Upon detection of a significant TP53 mutation, circulating cell-free DNA was scrutinized for the presence of the tumor-specific mutation. ctDNA was identified at a minimal allele frequency of 0.08% in 21 out of 30 processed plasma samples. Detectable ctDNA correlated with regional spread (N stage ≥ 1, p = 0.011) and poorer 5-year progression-free survival (20%, 95% CI 10.9 to 28.9, p = 0.034). The high-risk worst pattern of invasion (WPOI grade 4-5) and deep invasion were frequently found in patients whose ctDNA was detected (p = 0.087 and p = 0.072, respectively). Detecting mutated TP53 ctDNA was associated with poor progression-free survival and regional metastases, indicating its potential role as a prognostic biomarker. However, ctDNA detectability in early-stage disease and the mechanisms modulating its release into the bloodstream must be further elucidated.

Brain-Targeted Liposomes Loaded with Monoclonal Antibodies Reduce Alpha-Synuclein Aggregation and Improve Behavioral Symptoms in Parkinson's Disease.

Monoclonal antibodies (mAbs) hold promise in treating Parkinson's disease (PD), although poor delivery to the brain hinders their therapeutic application. In the current study, it is demonstrated that brain-targeted liposomes (BTL) enhance the delivery of mAbs across the blood-brain-barrier (BBB) and into neurons, thereby allowing the intracellular and extracellular treatment of the PD brain. BTL are decorated with transferrin to improve brain targeting through overexpressed transferrin-receptors on the BBB during PD. BTL are loaded with SynO4, a mAb that inhibits alpha-synuclein (AS) aggregation, a pathological hallmark of PD. It is shown that 100-nm BTL cross human BBB models intact and are taken up by primary neurons. Within neurons, SynO4 is released from the nanoparticles and bound to its target, thereby reducing AS aggregation, and enhancing neuronal viability. In vivo, intravenous BTL administration results in a sevenfold increase in mAbs in brain cells, decreasing AS aggregation and neuroinflammation. Treatment with BTL also improve behavioral motor function and learning ability in mice, with a favorable safety profile. Accordingly, targeted nanotechnologies offer a valuable platform for drug delivery to treat brain neurodegeneration.

Perineural invasion detection in pancreatic ductal adenocarcinoma using artificial intelligence.

Perineural invasion (PNI) refers to the presence of cancer cells around or within nerves, raising the risk of residual tumor. Linked to worse prognosis in pancreatic ductal adenocarcinoma (PDAC), PNI is also being explored as a therapeutic target. The purpose of this work was to build a PNI detection algorithm to enhance accuracy and efficiency in identifying PNI in PDAC specimens. Training used 260 manually segmented nerve and tumor HD images from 6 scanned PDAC cases; Analytical performance analysis used 168 additional images; clinical analysis used 59 PDAC cases. The algorithm pinpointed key areas of tumor-nerve proximity for pathologist confirmation. Analytical performance reached sensitivity of 88% and 54%, and specificity of 78% and 85% for the detection of nerve and tumor, respectively. Incorporating tumor-nerve distance in clinical evaluation raised PNI detection from 52 to 81% of all cases. Interestingly, pathologist analysis required an average of only 24 s per case. This time-efficient tool accurately identifies PNI in PDAC, even with a small training cohort, by imitating pathologist thought processes.

Artificial intelligence (AI) molecular analysis tool assists in rapid treatment decision in lung cancer: a case report.

Leptomeningeal involvement among non-small cell lung cancer (NSCLC) patients is an aggressive form of disease that requires quick and efficient treatment. In this case report, we describe a woman in her 40s with a presenting symptom of headache that ultimately was diagnosed as leptomeningeal spread from NSCLC adenocarcinoma. We identified EGFR mutation in less than 48 hours from the biopsy using imagene-artificial intelligence's real-time algorithmic solution on the pathological diagnostic slide.

Neoadjuvant Osimertinib Followed by Sequential Definitive Radiation Therapy and/or Surgery in Stage III Epidermal Growth Factor Receptor-Mutant Non-Small Cell Lung Cancer: An Open-Label, Single-Arm, Phase 2 Study.

PURPOSE: The treatment for unresectable, locally advanced stage III non-small cell lung cancer (NSCLC) is concurrent chemoradiation therapy (CRT) followed by consolidation durvalumab. This study aimed to evaluate the benefit of neoadjuvant osimertinib as an alternative therapy to this approach with the aim of reducing the radiation field. METHODS AND MATERIALS: This investigation was a nonrandomized, open-label, single-arm, phase 2, prospective, proof-of-concept study. Eligible patients were classified as having treatment-naïve, nonoperable, stage III epidermal growth factor receptor-mutant NSCLC. Patients received 80 mg of oral osimertinib daily for 12 weeks before definitive radiation therapy (RT) and/or surgery. The response was assessed at weeks 6 and 12. For responders, sequential definitive RT and/or surgery were planned. Nonresponders were started on standard CRT. After RT ± surgery or CRT, patients were followed for 2 years without adjuvant therapy. The primary endpoint was the objective response rate (ORR), with September 20, 2022, set as the cut-off for data collection. Secondary endpoints were safety and the gross tumor volume (GTV), planned tumor volume (PTV), and the percentage of total lung volume minus GTV exceeding 20 Gy (V20%) before versus after osimertinib. Exploratory analyses included assessments of the presence of plasma circulating tumor-free DNA (ctDNA) before osimertinib treatment, at weeks 6 and 12, at the end of RT, and 6 weeks post-RT. RESULTS: Twenty-four patients were included (19 women; median age, 73 years; range, 51-82 years). Nineteen of 24 had never smoked, 20 of 24 had adenocarcinoma, 16 of 24 had exon 19 deletions, and 8 of 24 had exon 21 mutations. Participants had stage IIIA (10), IIIB (9), or IIIC (5) disease. Three patients were excluded from the analysis (1 dropped out and 2 were still undergoing osimertinib treatment at the cut-off date). The ORR to induction osimertinib was 95.2% (17 partial response, 3 complete response, and 1 progressive disease). After induction osimertinib, 13 of 20 patients were definitively radiated, 3 of 20 underwent surgery, and 5 of 20 were excluded. Four patients were restaged as stage IV (contralateral ground-glass opacities responded to osimertinib), and 1 patient withdrew informed consent. Three patients underwent surgery, one of whom was treated with RT. Two patients achieved pT1aN0, and one achieved pathologic complete response. The median GTV, PTV, and V20% before osimertinib treatment were 47.4 ± 76.9 cm3 (13.5-234.9), 227.0 ± 258.8 cm3 (77.8-929.2), and 27.1 ± 16.4% (6.2-60.3), respectively. The values after osimertinib treatment were 27.5 ± 42.3 cm3 (2.99-137.7; -48 ± 20%; P = .02), 181.9 ±198.4 cm3 (54-718.1; -31 ± 20%; P = .01), and 21.8 ± 11.7% (9.1-44.15; -24 ± 40%; P = .04), respectively. PTV/GTV/V20% reduction was associated with tumor size and central location. The median follow-up time was 28.71 months (range, 0.4-45.1 months), and median disease-free survival was not reached (mean, 30.59; standard error, 3.94; 95% confidence interval, 22.86-38.31). ctDNA was detected in 5 patients; 4 of 5 were positive for ctDNA at baseline and became negative during osimertinib induction but were again positive after osimertinib treatment was terminated. Interestingly, 3 patients who were ctDNA negative at baseline became weakly positive after RT and then were negative at follow-up. No significant adverse events were reported during the osimertinib or radiation phases. CONCLUSIONS: Neoadjuvant osimertinib therapy is feasible in patients with stage III lung cancer NSCLC, followed by definitive radiation and/or surgery, with an ORR of 95.2% and an excellent safety profile. Osimertinib induction for 12 weeks before definitive radiation (chemo-free) significantly reduced the radiation field by nearly 50% with a linear association with tumor size. Further studies are needed to test this chemo-free approach for long-term outcomes before practices are changed.

Short Training Significantly Improves Ganglion Cell Detection Using an Algorithm-Assisted Approach.

CONTEXT.­: Medical education in pathology relies on the accumulation of experience gained through inspection of numerous samples from each entity. Acquiring sufficient teaching material for rare diseases, such as Hirschsprung disease (HSCR), may be difficult, especially in smaller institutes. The current study makes use of a previously developed decision support system using a decision support algorithm meant to aid pathologists in the diagnosis of HSCR. OBJECTIVE.­: To assess the effect of a short training session on algorithm-assisted HSCR diagnosis. DESIGN.­: Five pathologists reviewed a data set of 568 image sets (1704 images in total) selected from 50 cases by the decision support algorithm and were tasked with scoring the images for the presence or absence of ganglion cells. The task was repeated a total of 3 times. Each pathologist had to complete a short educational presentation between the second and third iterations. RESULTS.­: The training resulted in a significantly increased rate of correct diagnoses (true positive/negative) and a decreased need for referrals for expert consultation. No statistically significant changes in the rate of false positives/negatives were detected. CONCLUSIONS.­: A very short (<10 minutes) training session can greatly improve the pathologist's performance in the algorithm-assisted diagnosis of HSCR. The same approach may be feasible in training for the diagnosis of other rare diseases.

Digital PCR-Based Method for Detecting CDKN2A Loss in Brain Tumours.

INTRODUCTION: CDKN2A is a key tumour suppressor gene and loss of CDKN2A can be found in many tumours. In astrocytoma grade IV, CDKN2A is deleted in more than 50% of tumours. In many instances, low-grade gliomas with homozygous loss of CDKN2A behave like high grade tumours. The available techniques for CDKN2A loss are laborious, expensive, unreliable, or unavailable in most pathology institutes. Therefore, although it is essential for accurate brain tumour diagnosis, the routine diagnosis does not include testing for CDKN2A deletion. METHODS: We developed a digital polymerase chain reaction (dPCR) assay for CDKN2A loss detection. The assay is based on counting the copy number of CDKN2A gene and of a reference gene on the same chromosome. It was tested for the detection limit with regard to tumour content and minimal DNA quantity. It was then tested on 24 clinical samples with known CDKN2A status. Additionally, we tested 44 gliomas with unknown CDKN2A status. RESULTS: We found that the newly developed assay is reliable in tissue with more than 50% tumour content and more than 0.4 ng of DNA. The validation cohort showed complete concordance, and we were able to detect homozygous loss in 16 gliomas with unknown CDKN2A status. DISCUSSION: The method presented can give a fast, cost-effective, clinically reliable evaluation of CDKN2A loss in tissue with more than 50% tumour content. Its ability to work with old samples and with low amounts of DNA makes it the favoured assay in cases where other techniques fail.

Transient cell-in-cell formation underlies tumor relapse and resistance to immunotherapy.

Despite the remarkable successes of cancer immunotherapies, the majority of patients will experience only partial response followed by relapse of resistant tumors. While treatment resistance has frequently been attributed to clonal selection and immunoediting, comparisons of paired primary and relapsed tumors in melanoma and breast cancers indicate that they share the majority of clones. Here, we demonstrate in both mouse models and clinical human samples that tumor cells evade immunotherapy by generating unique transient cell-in-cell structures, which are resistant to killing by T cells and chemotherapies. While the outer cells in this cell-in-cell formation are often killed by reactive T cells, the inner cells remain intact and disseminate into single tumor cells once T cells are no longer present. This formation is mediated predominantly by IFNγ-activated T cells, which subsequently induce phosphorylation of the transcription factors signal transducer and activator of transcription 3 (STAT3) and early growth response-1 (EGR-1) in tumor cells. Indeed, inhibiting these factors prior to immunotherapy significantly improves its therapeutic efficacy. Overall, this work highlights a currently insurmountable limitation of immunotherapy and reveals a previously unknown resistance mechanism which enables tumor cells to survive immune-mediated killing without altering their immunogenicity.

Cancer immunotherapies use the body's own immune system to fight off cancer. But, despite some remarkable success stories, many patients only see a temporary improvement before the immunotherapy stops being effective and the tumours regrow. It is unclear why this occurs, but it may have to do with how the immune system attacks cancer cells. Immunotherapies aim to activate a special group of cells known as killer T-cells, which are responsible for the immune response to tumours. These cells can identify cancer cells and inject toxic granules through their membranes, killing them. However, killer T-cells are not always effective. This is because cancer cells are naturally good at avoiding detection, and during treatment, their genes can mutate, giving them new ways to evade the immune system. Interestingly, when scientists analysed the genes of tumour cells before and after immunotherapy, they found that many of the genes that code for proteins recognized by T-cells do not change significantly. This suggests that tumours' resistance to immune attack may be physical, rather than genetic. To investigate this hypothesis, Gutwillig et al. developed several mouse tumour models that stop responding to immunotherapy after initial treatment. Examining cells from these tumours revealed that when the immune system attacks, they reorganise by getting inside one another. This allows some cancer cells to hide under many layers of cell membrane. At this point killer T-cells can identify and inject the outer cell with toxic granules, but it cannot reach the cells inside. This ability of cancer cells to hide within one another relies on them recognising when the immune system is attacking. This happens because the cancer cells can detect certain signals released by the killer T-cells, allowing them to hide. Gutwillig et al. identified some of these signals, and showed that blocking them stopped cancer cells from hiding inside each other, making immunotherapy more effective. This new explanation for how cancer cells escape the immune system could guide future research and lead to new cancer treatments, or approaches to boost existing treatments. Understanding the process in more detail could allow scientists to prevent it from happening, by revealing which signals to block, and when, for best results.

Low frequency nanobubble-enhanced ultrasound mechanotherapy for noninvasive cancer surgery.

Scaling down the size of microbubble contrast agents to the nanometer level holds the promise for noninvasive cancer therapy. However, the small size of nanobubbles limits the obtained bioeffects as a result of ultrasound cavitation, when operating near the nanobubble resonance frequency. Here we show that coupled with low energy insonation at a frequency of 80 kHz, well below the resonance frequency of these agents, nanobubbles serve as noninvasive therapeutic warheads that trigger potent mechanical effects in tumors following a systemic injection. We demonstrate these capabilities in tissue mimicking phantoms, where a comparison of the acoustic response of micro- and nano-bubbles after insonation at a frequency of 250 or 80 kHz revealed that higher pressures were needed to implode the nanobubbles compared to microbubbles. Complete nanobubble destruction was achieved at a mechanical index of 2.6 for the 250 kHz insonation vs. 1.2 for the 80 kHz frequency. Thus, the 80 kHz insonation complies with safety regulations that recommend operation below a mechanical index of 1.9. In vitro in breast cancer tumor cells, the cell viability was reduced to 17.3 ± 1.7% of live cells. In vivo, in a breast cancer tumor mouse model, nanobubble tumor distribution and accumulation were evaluated by high frequency ultrasound imaging. Finally, nanobubble-mediated low frequency insonation of breast cancer tumors resulted in effective mechanical tumor ablation and tumor tissue fractionation. This approach provides a unique theranostic platform for safe, noninvasive and low energy tumor mechanotherapy.

Association between Nuclear Morphometry Parameters and Gleason Grade in Patients with Prostatic Cancer.

Objective: Gleason scoring system remains the pathological method of choice for prostate cancer (Pca) grading. However, this method of tumor tissue architectural structure grading is still affected by subjective assessment and might succumb to several disadvantages, mainly inter-observer variability. These limitations might be diminished by determining characteristic cellular heterogeneity parameters which might improve Gleason scoring homogeneity. One of the quantitative tools of tumor assessment is the morphometric characterization of tumor cell nuclei. We aimed to test the relationship between various morphometric measures and the Gleason score assigned to different prostate cancer samples. Materials and Methods: We reviewed 60 prostate biopsy samples performed at a tertiary uro-oncology center. Each slide was assigned a Gleason grade according to the International Society of Urological Pathology contemporary grading system by a single experienced uro-pathologist. Samples were assigned into groups from grades 3 to 5. Next, the samples were digitally scanned (×400 magnification) and sampled on a computer using Image-Pro-Plus software©. Manual segmentation of approximately 100 selected tumor cells per sample was performed, and a computerized measurement of 54 predetermined morphometric properties of each cell nuclei was recorded. These characteristics were used to compare the pathological group grades assigned to each specimen. Results: Initially, of the 54 morphometric parameters evaluated, 38 were predictive of Gleason grade (p < 0.05). On multivariate analysis, 7 independent parameters were found to be discriminative of different Pca grades: minimum radius shape, intensity­minimal gray level, intensity­maximal gray level, character­gray level (green), character­gray level (blue), chromatin color, fractal dimension, and chromatin texture. A formula to predict the presence of Gleason grade 3 vs. grades 4 or 5 was developed (97.2% sensitivity, 100% specificity). Discussion: The suggested morphometry method based on seven selected parameters is highly sensitive and specific in predicting Gleason score ≥ 4. Since discriminating Gleason score 3 from ≥4 is essential for proper treatment selection, this method might be beneficial in addition to standard pathological tissue analysis in reducing variability among pathologists.

The risk of PD-L1 expression misclassification in triple-negative breast cancer.

PURPOSE: Stratification of patients with triple-negative breast cancer (TNBC) for anti-PD-L1 therapy is based on PD-L1 expression in tumor biopsies. This study sought to evaluate the risk of PD-L1 misclassification. METHODS: We conducted a high-resolution analysis on ten surgical specimens of TNBC. First, we determined PD-L1 expression pattern distribution via manual segmentation and measurement of 6666 microscopic clusters of positive PD-L1 immunohistochemical staining. Then, based on these results, we generated a computer model to calculate the effect of the positive PD-L1 fraction, aggregate size, and distribution of PD-L1 positive cells on the diagnostic accuracy. RESULTS: Our computer-based model showed that larger aggregates of PD-L1 positive cells and smaller biopsy size were associated with higher fraction of false results (P < 0.001, P < 0.001, respectively). Additionally, our model showed a significant increase in error rate when the fraction of PD-L1 expression was close to the cut-off (error rate of 12.1%, 0.84%, and 0.65% for PD-L1 positivity of 0.5-1.5%, ≤ 0.5% ,and ≥ 1.5%, respectively, P < 0.0001). Interestingly, false positive results were significantly higher than false negative results (0.51-22.62%, with an average of 6.31% versus 0.11-11.36% with an average of 1.58% for false positive and false negative results, respectively, P < 0.05). Furthermore, heterogeneous tumors with different aggregate sizes in the same tumor, were associated with increased rate of false results in comparison to homogenous tumors (P < 0.001). CONCLUSION: Our model can be used to estimate the risk of PD-L1 misclassification in biopsies, with potential implications for treatment decisions.

FGFR Fusions as an Acquired Resistance Mechanism Following Treatment with Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors (EGFR TKIs) and a Suggested Novel Target in Advanced Non-Small Cell Lung Cancer (aNSCLC).

BACKGROUND: FGFR1/2/3 fusions have been reported infrequently in aNSCLC, including as a rare, acquired resistance mechanism following treatment with EGFR TKIs. Data regarding their prevalence and therapeutic implications are limited. METHODS: The Guardant Health (GH) electronic database (ED) was evaluated for cases of aNSCLC and FGFR2/3 fusions; FGFR2/3 fusion prevalence with and without a co-existing EGFR mutation was assessed. The ED of Tel-Aviv Sourasky Medical Center (TASMC, June 2020-June 2021) was evaluated for cases of aNSCLC and de novo FGFR1/2/3 fusions. Patients with EGFR mutant aNSCLC progressing on EGFR TKIs and developing an FGFR1/2/3 fusion were selected from the ED of Davidoff Cancer Center (DCC) and Oncology Department, Bnei-Zion hospital (BZ) (April 2014-April 2021). Clinicopathological characteristics, systemic therapies, and outcomes were assessed. RESULTS: In the GH ED (n = 57,445), the prevalence of FGFR2 and FGFR3 fusions were 0.02% and 0.26%, respectively. FGFR3-TACC3 fusion predominated (91.5%). In 23.8% of cases, FGFR2/3 fusions co-existed with EGFR sensitizing mutations (exon 19 del, 64.1%; L858R, 33.3%, L861Q, 2.6%). Among samples with concurrent FGFR fusions and EGFR sensitizing mutations, 41.0% also included EGFR resistant mutations. In TASMC (n = 161), 1 case of de novo FGFR3-TACC3 fusion was detected (prevalence, 0.62%). Of three patients from DCC and BZ with FGFR3-TACC3 fusions following progression on EGFR TKIs, two received EGFR TKI plus erdafitinib, an FGFR TKI, with clinical benefit duration of 13.0 and 6.0 months, respectively. CONCLUSIONS: Over 23% of FGFR2/3 fusions in aNSCLC may be associated with acquired resistance following treatment with EGFR TKIs. In this clinical scenario, a combination of EGFR TKIs and FGFR TKIs represents a promising treatment strategy.

Ex vivo organotypic cultures for synergistic therapy prioritization identify patient-specific responses to combined MEK and Src inhibition in colorectal cancer.

Translating preclinical studies to effective treatment protocols and identifying specific therapeutic responses in individuals with cancer is challenging. This may arise due to the complex genetic makeup of tumor cells and the impact of their multifaceted tumor microenvironment on drug response. To find new clinically relevant drug combinations for colorectal cancer (CRC), we prioritized the top five synergistic combinations from a large in vitro screen for ex vivo testing on 29 freshly resected human CRC tumors and found that only the combination of mitogen-activated protein kinase kinase (MEK) and proto-oncogene tyrosine-protein kinase Src (Src) inhibition was effective when tested ex vivo. Pretreatment phosphorylated Src (pSrc) was identified as a predictive biomarker for MEK and Src inhibition only in the absence of KRASG12 mutations. Overall, we demonstrate the potential of using ex vivo platforms to identify drug combinations and discover MEK and Src dual inhibition as an effective drug combination in a predefined subset of individuals with CRC.

Implementation of Comprehensive Genomic Profiling in Ovarian Cancer Patients: A Retrospective Analysis.

Comprehensive genomic profiling (CGP) allows for the detection of driver alterations at high resolution, but the limited number of approved targeted therapies and their high costs have contributed to its limited clinical utilization. We retrospectively compared data of 946 women with ovarian cancer (11.4% were referred to CGP, and 88.6% served as control) to examine whether CGP provides a prognosis benefit. Patient baseline parameters were similar between the groups. Cox regression analysis adjusted for age, disease stage at diagnosis, and recurrence status showed statistically significantly longer median overall survival (mOS) in the CGP group versus the control (73.4 versus 54.5 months, p < 0.001). Fifty-four patients (52.9%) had actionable mutations with potential treatments; twenty-six (48.2%) were treated with matched targeted therapy, showing a trend for longer mOS than the eighty-six women in the CGP group who were not given a suggested treatment (105.5 versus 63.6 months, p = 0.066). None of the genomic alterations predicted metastasis location. CCNE1 amplification and KRAS mutations were associated with shorter mOS. Patients with tumor mutation burden ≥4 mutations/megabase had longer mOS. High loss of heterozygosity was associated with longer mOS (99.0 versus 48.2 months, p = 0.004). CGP testing may provide both prognostic and predictive insights for treatment of patients with ovarian cancer. Prospective studies of larger cohorts are warranted.